Most people seem to agree that healthcare is ripe for innovation, and badly needs it. Lots of people are talking up two potential sources for that innovation: Big Data and Cognitive Computing.
I'm strongly in favor of data, the bigger the better. But is the Big Data movement going to make a difference? I'm strongly in favor of cognition, computing, and computing that is smarter rather than dumber. But is the Cognitive Computing movement likely to make a difference? Here's a summary of some thoughts.
Here is a description of the core process automation process implemented by a company I've invested in, Candescent Health. It describes the process that can and should be applied to all of health care.
The point isn't that there's data and analytics - the point is that there's a closed-loop process of continuous improvement where actions are based on rules. This is the framework that is required to make anything happen. Without it, you can't put your proposed new clinical action into practice with double-blind A-B test and see if the results of your analytics actually deliver benefits in the real world! Or even just deploy it!
Here is the story illustrated by Mt Sinai hospital about how everyone focuses on "innovation" and fancy new things, when just having the computer systems run reliability has a huge impact on patients - and unless those systems run, the results of fancy new analytics can't be delivered to benefit patients.
If the car won't start or run reliably, who cares how good the fancy sound and navigation systems are?
I love data and analytics. But doesn't it make sense to focus on getting the operational computer systems to actually run well before moving on to the fancy stuff?
In fact, just about anything you do with healthcare data that is going to be brought to the front line of care requires functioning computer systems to be able to pull off - the big healthcare systems pay Greenwich CT prices and get trailer park results.
Both data warehousing and the fancy new Big Data movement share the under-appreciated problem of getting good quality data in analytics-ready form. Sounds simple, but the difficulties make progress a grinding crawl on many efforts. See this for example.
Massive data sets have built-in problems that make it hard to get actionable results.
Skepticism about Cognitive Computing in health care is warranted. There is a rich history of over-promise and under-deliver for AI efforts in general.
Meanwhile, there are proven gems in the medical literature just waiting to be disseminated to the front lines of health care via point-of-care computer systems that are languishing in journals.
There are lots of practical, tangible ways to make things better, in spite of all the obstacles to change pervading our healthcare system. Here are some examples of people doing the right thing, all them with investments by Oak HC/FT:
- Candescent delivers better imaging results less expensive by applying basic continuous-improvement workflow automation.
- VillageMD delivers better results with lower cost by feeding back results and advice to PCP's.
- Aspire delivers better results at lower cost for end of life - by having one person be in charge, managing everything from the patient point of view.
- Quartet makes a difference by applying behavior health as needed to help other conditions.
These companies embody some common themes:
- Knock down the silos, have a patient-experience-centric point of view.
- Applying common sense has huge benefits.
- Focus on delivering results to the front line (patient) is hard but necessary.
- A system of continuous learning and delivery is a pre-condition to delivering any results of analytics for patient benefit.
The big hot topics in healthcare of Big Data and Cognitive Computing are little more than fashion statements. Data, of course, is a good thing; so is having computers do smart things. But without doing some basic blocking-and-tackling and applying some practical common sense, a great deal of time, money and energy will be spent accomplishing nothing.